The invention relates generally to the field of image reconstruction in computed tomography (CT) imaging systems and more particularly to a method and system for reconstructing image volumes from helical scan acquisitions.
CT systems operate by projecting fan-shaped or cone-shaped X-ray beams through an object. The X-ray beams are generated by an X-ray source, and are generally collimated prior to passing through the object being scanned. The attenuated beams are then detected by a detector. The detector produces a signal based on the intensity of the attenuated X-ray beams, and the signals are processed to produce projection data. CT systems acquire data continuously, at discrete image view frames corresponding to specific angular positions, as the source and detector rotate about the object being scanned.
In helical cone-beam CT systems, the X-ray source and the detectors are mounted on a rotating gantry while the object is moved axially at a uniform rate. In helical modes of operation, the X-ray source and detector describe a helical trajectory relative to the object; the detector measures the transmitted radiation on a part of a cone of rays emanating from the X-ray source. The resulting data set contains a large quantity of data points indicative of the intensity of radiation received by the detector elements at each of the angular positions. Helical cone-beam CT systems have faster scan times and have the potential to cover large objects, with just a few gantry rotations, depending on the axial coverage of the detectors.
A number of exact reconstruction algorithms have been developed for the reconstruction of cone-beam projection data acquired in a helical mode. Cone-beam reconstruction algorithms are known to be mathematically exact in the absence of noise and discretization (sampling) effects, and generally produce images of high quality when used on real data. However, in some applications (such as industrial CT inspections), requiring high-throughput imaging to be performed on large objects, cone-beam reconstruction of projection data is expensive in terms of computation, data access and latency requirements.
To reduce the complexity associated with cone-beam reconstruction, image reconstruction techniques based on helical interpolation and two-dimensional (2D) Filtered Back Projection (FBP) reconstruction may be used, to interpolate one or more helical views to approximate a corresponding axial or circular view. As is known to those skilled in the art, circular or axial scan approximations of helical views retain good image quality at the center of the reconstructed image volume, typically referred to as the “iso-center”. However, as the distance of the image pixel from the “iso-center” increases, the image quality decreases and circular or axial scan approximations tend to become de-focused, thereby introducing image artifacts. Therefore, image reconstruction techniques based on helical interpolation and two-dimensional (2D) FBP reconstruction, when used in a manner as described above, may introduce image artifacts, particularly, in systems with topologies requiring a large field of view and large detector extent or axial coverage.
It would be desirable to develop a computationally efficient technique based on helical interpolation and two-dimensional (2D) FBP reconstruction algorithms, for the reconstruction of large image volumes in high-throughput applications. In addition, it would be desirable to develop a computationally efficient technique for the reconstruction of large image volumes acquired from helical scan acquisitions, with reduced image artifacts and optimized image quality throughout the field of view.